1. DeepDive Case Study Dongfang Xu School of Information

2. Outline Deepdive Installation & Configuration – Deepdive application structure – Deepdive configuration Case Study – Create & load input – Candidate Generation and Feature Extraction – Inference rules – Running and getting results

3. Deepdive Installation Mac with homebrew – Open your terminal and run this: bash <(curl -fsSL deepdive.stanford.edu/install) – Install

4. Deepdive Configuration Structure of Deepdive – Deepdive application is a is a directory that contains configure files, input folder, log files, and user defined extraction feature:

5. Case Study Create Input – Input is marked up as below:

6. Case Study Preprocess Input – DeepDive provides a pre-built extractor which uses the Stanford CoreNLP Kit to split documents into sentences and tag them.

7. Case Study Load Input – User created Schema – Load preprocess file deepdive sql "COPY sentences FROM STDIN CSV" <./input/sentences_dump.csv

8. Case Study Extractor defined in file “deepdive.conf” User defined function (udf) embedded in Extractor DeepDive allows users to define different pipelines for these extractors

9. Case Study Mention extraction – 88266 people mention

10. Case Study a relation candidate is a pair of person mentions in the same sentence. use distant supervision rules that generate mention-level training data

11. Case Study User defined Feature 1 the bag of words between the two mentions; 2 the number of words between two phases; 3 whether the last word of the two persons' name (last name) is the same.

12. Case Study The weight of feature is learned from the training data

13. Case Study The weight of feature is learned from the training data This rule generates a model similar to a logistic regression classifier

14. Case Study

15. Thank you! Q&A